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Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

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arxiv 2309.05444 v1 pith:ND2WWIX3 submitted 2023-09-11 cs.CL cs.LG

Ted Zadouri , Ahmet Üstün , Arash Ahmadian , Beyza Ermiş , Acyr Locatelli , Sara Hooker This is my paper
classification cs.CLcs.LG
keywords expertsarchitecturemixtureextremelyfine-tuninglightweightlimitparameter
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The Mixture of Experts (MoE) is a widely known neural architecture where an ensemble of specialized sub-models optimizes overall performance with a constant computational cost. However, conventional MoEs pose challenges at scale due to the need to store all experts in memory. In this paper, we push MoE to the limit. We propose extremely parameter-efficient MoE by uniquely combining MoE architecture with lightweight experts.Our MoE architecture outperforms standard parameter-efficient fine-tuning (PEFT) methods and is on par with full fine-tuning by only updating the lightweight experts -- less than 1% of an 11B parameters model. Furthermore, our method generalizes to unseen tasks as it does not depend on any prior task knowledge. Our research underscores the versatility of the mixture of experts architecture, showcasing its ability to deliver robust performance even when subjected to rigorous parameter constraints. Our code used in all the experiments is publicly available here: https://github.com/for-ai/parameter-efficient-moe.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FlexOlmo: Open Language Models for Flexible Data Use

    cs.CL 2025-07 conditional novelty 7.0 of 10

    FlexOlmo merges independently trained language-model experts, trained on private data, into a single mixture-of-experts model without joint training.

  2. SAM+D: Parameter-Efficient Dimensional Lifting of SAM-Family Models via Depth-Routed LoRA and Depth Shifting

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Depth-routed LoRA and a depth-shift module lift frozen SAM and SAM2 to 3D and 3D+T segmentation using less than ~3.7% trainable parameters.

  3. CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging

    cs.CL 2026-02 conditional novelty 6.0 of 10

    CoMoL represents every LoRA expert as a shared-basis core matrix and merges token-selected experts in that core space, reaching standard LoRA parameter counts while outperforming MoE-LoRA baselines on math and code.

  4. CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the Edge

    cs.AR 2025-06 conditional novelty 5.0 of 10

    CLONE combines generative pruning, LoRA adapters, a parameter-free Mixture-of-Experts router, and learning-based DVFS to make LLM inference on edge devices faster and more energy-efficient, claiming up to 11.92x speed...

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